Denoising is a common preprocessing step prior to the analysis and interpretation of hyperspectral images (HSIs). However, the vast majority of methods typically adopted for HSI denoising exploit architectures originally developed for grayscale or RGB images, exhibiting limitations when processing high-dimensional HSI data cubes. In particular, traditional methods do not take into account the high spectral correlation between adjacent bands in HSIs, which leads to unsatisfactory denoising performance as the rich spectral information present in HSIs is not fully exploited. To overcome this limitation, this article considers deep learning models—such as convolutional neural networks (CNNs)—to perform spectral–spatial HSI denoising. The proposed model, called HSI single denoising CNN (HSI-SDeCNN), efficiently takes into consideration both the spatial and spectral information contained in HSIs. Experimental results on both synthetic and real data demonstrate that the proposed HSI-SDeCNN outperforms other state-of-the-art HSI denoising methods. Source code: https://github.com/mhaut/HSI-SDeCNN

中文翻译：

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用于高光谱图像去噪的单一模型 CNN

去噪是分析和解释高光谱图像 (HSI) 之前的常见预处理步骤。然而，绝大多数通常用于 HSI 去噪的方法利用最初为灰度或 RGB 图像开发的架构，在处理高维 HSI 数据立方体时表现出局限性。特别是，传统方法没有考虑到 HSI 中相邻频段之间的高光谱相关性，由于没有充分利用 HSI 中存在的丰富光谱信息，这导致去噪性能不理想。为了克服这一限制，本文考虑使用深度学习模型（例如卷积神经网络 (CNN)）来执行谱空间 HSI 去噪。提出的模型称为 HSI 单去噪 CNN (HSI-SDeCNN)，有效地考虑了 HSI 中包含的空间和光谱信息。合成数据和真实数据的实验结果表明，所提出的 HSI-SDeCNN 优于其他最先进的 HSI 去噪方法。源代码：https://github.com/mhaut/HSI-SDeCNN